Hagio, T., Moody, J.B., Poitrasson-Rivière, A. et al. Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial. Eur J Nucl Med Mol Imaging 50, 1028–1033 (2023).
Description:
This study assesses the generalizability of a deep learning-based attenuation correction (DLAC) method for SPECT myocardial perfusion imaging (MPI) across multiple centers with varying scanner models and protocols evaluating for coronary artery disease (CAD).
Clinical Relevance:
The clinical implementation of DLAC in SPECT MPI can substantially enhance the accuracy and reliability of CAD diagnosis. The study’s findings indicate that DLAC performs well across different clinical settings, scanner models, and imaging protocols.
Partners in Research:
INVIA Medical Imaging Solutions, GE Healthcare, and the University of Michigan collaborated on this research.
